DBMS > SAP HANA vs. Splice Machine vs. Tkrzw vs. WakandaDB

System Properties Comparison SAP HANA vs. Splice Machine vs. Tkrzw vs. WakandaDB

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Editorial information provided by DB-Engines

Name

Description

In-memory, column based data store. Available as appliance or cloud service

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Relational DBMS

Key-value store

Object oriented DBMS

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

www.sap.com/products/hana.html

splicemachine.com

dbmx.net/tkrzw

wakanda.github.io

Technical documentation

help.sap.com/hana

splicemachine.com/how-it-works

wakanda.github.io/doc

Developer

SAP

Splice Machine

Mikio Hirabayashi

Wakanda SAS

Initial release

2010

2014

2020

2012

Current release

2.0 SPS07 (April 4, 2023), April 2023

3.1, March 2021

0.9.3, August 2020

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

Apache Version 2.0

Open Source

AGPLv3, extended commercial license available

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

C++, JavaScript

Server operating systems

Appliance or cloud-service

Linux
OS X
Solaris
Windows

Linux
macOS

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

RESTful HTTP API

Supported programming languages

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

C++
Java
Python
Ruby

JavaScript

Server-side scripts

Stored procedures

SQLScript, R

yes

Java

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using specific database classes

User concepts

Access control

yes

Access rights for users, groups and roles according to SQL-standard

yes

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